


"""simple docstring"""






import math




class 							A_      :


  def __init__(			self:						int							,__lowerCAmelCase:						str=0    ):  # a graph with Node 0,1,...,N-1







     '''simple docstring'''







     _lowerCamelCase   :				Tuple							=     n
     _lowerCamelCase   :				Any							=     [
         [math.inf for j in range(0							,_A    )] for i in range(0							,_A    )
     ]  # adjacency matrix for weight
     _lowerCamelCase   :				Any							=     [
         [math.inf for j in range(0							,_A    )] for i in range(0							,_A    )
     ]  # dp[i][j] stores minimum distance from i to j


  def 							_lowercase					(			self:						Optional[int]							,__lowerCAmelCase:						str							,__lowerCAmelCase:						int							,__lowerCAmelCase:						Optional[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				str							=     w


  def 							_lowercase					(			self:						List[Any]    ):







     '''simple docstring'''







     for k in range(0							,self.n    ):
        for i in range(0							,self.n    ):
           for j in range(0							,self.n    ):
              _lowerCamelCase   :				Any							=     min(self.dp[i][j]							,self.dp[i][k] + self.dp[k][j]    )






  def 							_lowercase					(			self:						str							,__lowerCAmelCase:						List[Any]							,__lowerCAmelCase:						Tuple    ):







     '''simple docstring'''







     return self.dp[u][v]


if __name__ == "__main__":
  _lowerCAmelCase							:    int					   =	Graph(5)
  graph.add_edge(0, 2, 9)
  graph.add_edge(0, 4, 10)
  graph.add_edge(1, 3, 5)
  graph.add_edge(2, 3, 7)
  graph.add_edge(3, 0, 10)
  graph.add_edge(3, 1, 2)
  graph.add_edge(3, 2, 1)
  graph.add_edge(3, 4, 6)
  graph.add_edge(4, 1, 3)
  graph.add_edge(4, 2, 4)
  graph.add_edge(4, 3, 9)
  graph.floyd_warshall()
  graph.show_min(1, 4)
  graph.show_min(0, 3)







'''simple docstring'''





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def __UpperCAmelCase   (					A					:			Optional[int]		)    ->   List[Any]:
			# This defines a "chinese character" as anything in the CJK Unicode block:
			#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
			#
			# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
			# despite its name. The modern Korean Hangul alphabet is a different block,
			# as is Japanese Hiragana and Katakana. Those alphabets are used to write
			# space-separated words, so they are not treated specially and handled
			# like the all of the other languages.
			if (
			    (cp >= 0X4E_00 and cp <= 0X9F_FF)
			    or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
			    or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
			    or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
			    or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
			    or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
			    or (cp >= 0XF9_00 and cp <= 0XFA_FF)
			    or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
			):  #
						return True

			return False
def __UpperCAmelCase   (					A					:			str		)    ->   Optional[Any]:
			# word like '180' or '身高' or '神'
			for char in word:
						UpperCAmelCase_      :       str          =       ord(A		)
						if not _is_chinese_char(A		):
									return 0
			return 1
def __UpperCAmelCase   (					A					:			List[str]		)    ->   Dict:
			UpperCAmelCase_      :       Optional[Any]          =       set()

			for token in tokens:
						UpperCAmelCase_      :       str          =       len(A		) > 1 and is_chinese(A		)
						if chinese_word:
									word_set.add(A		)
			UpperCAmelCase_      :       Optional[int]          =       list(A		)
			return word_list
def __UpperCAmelCase   (					A					:			List[str]			,       A					:			set()		)    ->   Optional[Any]:
			if not chinese_word_set:
						return bert_tokens
			UpperCAmelCase_      :       Dict          =       max([len(A		) for w in chinese_word_set]		)

			UpperCAmelCase_      :       List[str]          =       bert_tokens
			UpperCAmelCase_    ,      UpperCAmelCase_      :       List[Any]          =       0, len(A		)
			while start < end:
						UpperCAmelCase_      :       str          =       True
						if is_chinese(bert_word[start]		):
									UpperCAmelCase_      :       str          =       min(end - start			,       A		)
									for i in range(A			,       1			,       -1		):
												UpperCAmelCase_      :       Tuple          =       ''''''.join(bert_word[start : start + i]		)
												if whole_word in chinese_word_set:
															for j in range(start + 1			,       start + i		):
																		UpperCAmelCase_      :       Union[str, Any]          =       '''##''' + bert_word[j]
															UpperCAmelCase_      :       Any          =       start + i
															UpperCAmelCase_      :       Optional[int]          =       False
															break
						if single_word:
									start += 1
			return bert_word
def __UpperCAmelCase   (					A					:			List[str]			,       A					:			LTP			,       A					:			BertTokenizer		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       Optional[int]          =       []

			for i in range(0			,       len(A		)			,       1_0_0		):
						UpperCAmelCase_      :       int          =       ltp_tokenizer.pipeline(lines[i : i + 1_0_0]			,       tasks=['''cws''']		).cws
						UpperCAmelCase_      :       Any          =       [get_chinese_word(A		) for r in res]
						ltp_res.extend(A		)
			assert len(A		) == len(A		)

			UpperCAmelCase_      :       Tuple          =       []
			for i in range(0			,       len(A		)			,       1_0_0		):
						UpperCAmelCase_      :       Optional[int]          =       bert_tokenizer(lines[i : i + 1_0_0]			,       add_special_tokens=A			,       truncation=A			,       max_length=5_1_2		)
						bert_res.extend(res['''input_ids''']		)
			assert len(A		) == len(A		)

			UpperCAmelCase_      :       Any          =       []
			for input_ids, chinese_word in zip(A			,       A		):
						UpperCAmelCase_      :       Union[str, Any]          =       []
						for id in input_ids:
									UpperCAmelCase_      :       Union[str, Any]          =       bert_tokenizer._convert_id_to_token(A		)
									input_tokens.append(A		)
						UpperCAmelCase_      :       List[str]          =       add_sub_symbol(A			,       A		)
						UpperCAmelCase_      :       Any          =       []
						# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
						for i, token in enumerate(A		):
									if token[:2] == "##":
												UpperCAmelCase_      :       int          =       token[2:]
												# save chinese tokens' pos
												if len(A		) == 1 and _is_chinese_char(ord(A		)		):
															ref_id.append(A		)
						ref_ids.append(A		)

			assert len(A		) == len(A		)

			return ref_ids
def __UpperCAmelCase   (					A					:			List[Any]		)    ->   Tuple:
			# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
			# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
			with open(args.file_name			,       '''r'''			,       encoding='''utf-8'''		) as f:
						UpperCAmelCase_      :       Optional[Any]          =       f.readlines()
			UpperCAmelCase_      :       List[str]          =       [line.strip() for line in data if len(A		) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
			UpperCAmelCase_      :       Tuple          =       LTP(args.ltp		)  # faster in GPU device
			UpperCAmelCase_      :       Union[str, Any]          =       BertTokenizer.from_pretrained(args.bert		)

			UpperCAmelCase_      :       Optional[int]          =       prepare_ref(A			,       A			,       A		)

			with open(args.save_path			,       '''w'''			,       encoding='''utf-8'''		) as f:
						UpperCAmelCase_      :       str          =       [json.dumps(A		) + '''\n''' for ref in ref_ids]
						f.writelines(A		)


if __name__ == "__main__":
						_UpperCamelCase						:						Optional[Any]  =					argparse.ArgumentParser(description='prepare_chinese_ref')
						parser.add_argument(
						    '--file_name',
						    required=False,
						    type=str,
						    default='./resources/chinese-demo.txt',
						    help='file need process, same as training data in lm',
						)
						parser.add_argument(
						    '--ltp',
						    required=False,
						    type=str,
						    default='./resources/ltp',
						    help='resources for LTP tokenizer, usually a path',
						)
						parser.add_argument(
						    '--bert',
						    required=False,
						    type=str,
						    default='./resources/robert',
						    help='resources for Bert tokenizer',
						)
						parser.add_argument(
						    '--save_path',
						    required=False,
						    type=str,
						    default='./resources/ref.txt',
						    help='path to save res',
						)

						_UpperCamelCase						:						Any  =					parser.parse_args()
						main(args)


